Error-related potentials are considered an important neuro-correlate for monitoring human intentionality in decision-making, human-human, or human-machine interaction scenarios. Multiple methods have been proposed in order to improve the recognition of human intentions. Moreover, current brain-computer interfaces are limited in the identification of human errors by manual tuning of parameters (e.g., feature/channel selection), thus selecting fronto-central channels as discriminative features within-subject. In this paper, we propose the inclusion of error-related potential activity as a generalized two-dimensional feature set and a Convolutional Neural Network for classification of EEG-based human error detection. We evaluate this pipeline using the BNCI2020 - Monitoring Error-Related Potential dataset obtaining a maximum error detection accuracy of 79.8% in a within-session 10-fold cross-validation modality, and outperforming current state of the art.

Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks / Mayor Torres, J. M.; Clarkson, T.; Stepanov, E. A.; Luhmann, C. C.; Lerner, M. D.; Riccardi, G.. - (2018), pp. 360-363. (Intervento presentato al convegno EMBC 2018 tenutosi a Honolulu nel 18th-21st July 2018) [10.1109/EMBC.2018.8512183].

Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks

Mayor Torres J. M.;Stepanov E. A.;Riccardi G.
2018-01-01

Abstract

Error-related potentials are considered an important neuro-correlate for monitoring human intentionality in decision-making, human-human, or human-machine interaction scenarios. Multiple methods have been proposed in order to improve the recognition of human intentions. Moreover, current brain-computer interfaces are limited in the identification of human errors by manual tuning of parameters (e.g., feature/channel selection), thus selecting fronto-central channels as discriminative features within-subject. In this paper, we propose the inclusion of error-related potential activity as a generalized two-dimensional feature set and a Convolutional Neural Network for classification of EEG-based human error detection. We evaluate this pipeline using the BNCI2020 - Monitoring Error-Related Potential dataset obtaining a maximum error detection accuracy of 79.8% in a within-session 10-fold cross-validation modality, and outperforming current state of the art.
2018
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Piscataway, NJ
IEEE
978-1-5386-3646-6
Mayor Torres, J. M.; Clarkson, T.; Stepanov, E. A.; Luhmann, C. C.; Lerner, M. D.; Riccardi, G.
Enhanced Error Decoding from Error-Related Potentials using Convolutional Neural Networks / Mayor Torres, J. M.; Clarkson, T.; Stepanov, E. A.; Luhmann, C. C.; Lerner, M. D.; Riccardi, G.. - (2018), pp. 360-363. (Intervento presentato al convegno EMBC 2018 tenutosi a Honolulu nel 18th-21st July 2018) [10.1109/EMBC.2018.8512183].
File in questo prodotto:
File Dimensione Formato  
EMBC18-Enhanced-Error-Decoding.pdf

Open Access dal 01/01/2021

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 787.47 kB
Formato Adobe PDF
787.47 kB Adobe PDF Visualizza/Apri
08512183.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.25 MB
Formato Adobe PDF
1.25 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/250218
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 15
  • ???jsp.display-item.citation.isi??? ND
social impact